1. Technical Field of the Invention
The present invention relates in general to the field of communications, and in particular, by way of example but not limitation, to determining correlations between received sequences and multiple training sequences in an efficient manner that minimizes the number of required mathematical operations.
2. Description of Related Art
Many people and organizations increasingly rely on wireless communication for safety, convenience, and productivity, as well as simple conversational pleasure. One example of wireless communication is cellular communication. Cellular phone use has proliferated as the size of mobile terminals (MTs) and the cost for service subscriptions as well as air time have decreased. As cellular phone use has proliferated and the allocated radio frequency (RF) spectrum has become correspondingly more crowded, it has become ever more important to efficiently utilize the available RF spectrum.
Cellular communication systems, such as the Global System for Mobile Communications (GSM), therefore need to efficiently reuse the RF spectrum in order to attempt to maximize the capacity of the system. Consequently, such systems are often Carrier-to-Interference (C/I) limited; in other words, co-channel interferers are often the factor limiting capacity in a system. In conventional systems, co-channel interferers are addressed in the demodulation process as unknown white noise.
However, it has been observed that better receiver performance (e.g., a lower bit error rate (BER)) can be obtained by using knowledge of the interfering signal in the data recovery process. In fact, studies have indicated that receiver performance in C/I-limited cellular systems can be significantly improved by using co-channel interference rejection (IR) techniques. Co-channel IR techniques require an initial identification of the interferers, which may be accomplished by finding the training sequences of all received interfering signals. The training sequences may be found by correlating each received sequence with all possible training sequences that are used in the C/I-limited cellular system.
Correlation of a known (e.g., training) sequence with a sequence of samples can be computed according to the correlation definition; in other words, the correlation may be computed as an inner product of the known sequence with the sample sequence at each of a number of offsets. Unfortunately, the computational complexity of the correlation calculation grows with the training sequence length, with the number of training sequences, and with the number of offsets considered. It should be noted that a large number of offsets must ordinarily be considered because different base stations (BSs) will not generally be synchronized. The resulting large number of operations required for solving the correlation equation causes a high cost in terms of hardware requirements, processing time delays, and power dissipation demands.